CCNP Meetings

Rita Goldstein
Department of Psychiatry
Icahn School of Medicine at Mount Sinai

Drug addiction is a chronically relapsing disorder characterized by compulsive drug use despite catastrophic personal consequences (e.g., loss of family, job) and even when the substance is no longer perceived as pleasurable. In this talk, Dr. Goldstein will present results of human neuroimaging studies, utilizing a multimodal approach (neuropsychology, functional magnetic resonance imaging, positron emission tomography, event-related potentials recordings), to explore the neurobiology underlying the core psychological impairments in drug addiction (impulsivity, drive/motivation, insight/awareness) as associated with its clinical symptomatology (intoxication, craving, bingeing, withdrawal). The focus of this talk is on understanding the role of the dopaminergic mesocorticolimbic circuit, and especially the prefrontal cortex, in higher-order executive dysfunction (e.g., disadvantageous decision-making such as trading a car for a couple of cocaine hits) in drug addicted individuals. The theoretical model that guides the presented research is called iRISA (Impaired Response Inhibition and Salience Attribution), postulating that abnormalities in the orbitofrontal cortex and anterior cingulate cortex, as related to dopaminergic dysfunction, contribute to the core clinical symptoms in drug addiction. Specifically, Dr. Goldstein’s multi-modality program of research is guided by the underlying working hypothesis that drug addicted individuals disproportionately attribute reward value to their drug of choice at the expense of other potentially but no-longer-rewarding stimuli, with a concomitant decrease in the ability to inhibit maladaptive drug use. In this talk Dr. Goldstein will also explore whether treatment (as usual) and 6-month abstinence enhance recovery in these brain-behavior compromises in treatment seeking cocaine addicted individuals. Promising novel fMRI studies, which combine pharmacological (i.e., oral methylphenidate, or RitalinTM) and salient cognitive tasks or functional connectivity during resting-state, will be discussed as examples for using neuroimaging in the empirical guidance for the development of effective neurorehabilitation strategies in cocaine addiction.

Eileen Kowler
Department of Psychology
Rutgers University

It has been known since the classical work of Dodge (1930) and Westheimer (1954) that smooth pursuit eye movements anticipate the future direction of motion of targets. Anticipation in smooth pursuit is somewhat surprising because pursuit is traditionally viewed as a reactive process that acts to compensate for the motion of the target across the retina. Prior attempts to account for anticipatory pursuit have emphasized either rote learning of the motion path, associative learning, or short-term extrapolation of the motion. I will describe results of several experiments showing that anticipatory smooth eye movements can be evoked by visual cues, or by knowledge of the past history of motion (in the absence of overt cues), or by one’s own intentions to alter the path of target motion. Cues that illustrate the motion path by virtue of their perceptual structure are far more effective than cues that are arbitrarily linked to the motion path, or beliefs derived from simply telling the pursuer about the future motion path. Taken together (and considering the intriguing patterns of anticipatory pursuit movements found in other species), these results suggest that prediction is as fundamental to smooth pursuit as it is to the operation of many other visuo-motor behaviors. The pursuit system appears to be organized so as to take advantage of any source of information that allows future motion to be predicted with a reasonably high level of confidence.

Ken Norman
Princeton Neuroscience Institute
Princeton University

About 10 years ago, our lab built a neural network model of how competitive neural dynamics drive learning (Norman, Newman, & Perotte, 2005; Norman et al., 2006; Norman, Newman, & Detre, 2007). This model predicts a U-shaped relationship between neural activation and learning, whereby strong activation causes strengthening of synaptic connections, moderate activation causes weakening of synaptic connections, and lower levels of activation result in no change to synaptic connections. To test this prediction, we have run several studies where we use pattern classifiers (applied to fMRI and EEG data) to track the activation of the competing memories, and then we relate these competitive dynamics to subsequent memory performance. If — according to the classifier — a memory activates to a moderate degree, our theory predicts that it will be weakened and, through this, it will subsequently become harder to retrieve. I will present evidence in support of this prediction from studies using several different paradigms. I will also present more recent data showing how competitive dynamics, coupled with interleaved learning, can result in differentiation of competing memories. I will conclude by discussing possible applications to clinical phenomena.

Sam Wang
Princeton Neuroscience Institute
Princeton University

Although the cerebellum is mainly known as a sensory/motor structure, its influence and connections reach many regions known for cognitive function, emotion, and reward. I will present evidence for the idea that the cerebellum acts during sensitive periods to shape the developing brain. This hypothesis can explain a wide range of observations in autism, and may illuminate how the brain’s wiring is shaped by early-life sensory experience.

Deepu Murty
Department of Psychiatry
University of Pittsburgh

As we navigate through the world, we are inundated with immense amounts of information—to much information to veridically encode into long-term memory. Rather than attempt to encode all of this information, memory is adaptive. Information that is most relevant to achieving future goals is prioritized in long-term memory. In my talk, I will present a series of behavioral and neuroimaging studies that characterize (1) how the neural systems underlying motivation facilitate episodic memory and choice behavior and (2) how aberrant connectivity within this circuit results in maladaptive behavior in psychosis. Together, these studies support a model in which individuals tailor their memories of the environment to their goal states during encoding, which provides a foundation of information to support both future adaptive and maladaptive behavior.

Quentin Huys
Translational Neuromodeling Unit
ETH Zurich and University of Zurich

Emotions are complex and hard to define. But emotions are extremely important to psychiatry, and hence it is important for computational psychiatry to understand to what extent the prominent theoretical frameworks are able to capture important features of emotions. This talk will be divided into three parts.

First, I will describe some work on Pavlovian influences on simple and complex choice behaviour. Second, I will briefly review decision-making work on depression, arguing that a model-based account is necessary. Third, I will introduce a speculative notion of emotions as implementing approximate metareasoning strategies and discuss how this could qualitatively account for important features of emotions and emotion regulation.

Peter Uhlhaas
Department of Psychology
University of Glasgow

A considerable body of work over the last 10 years combining non-invasive electrophysiology (electroencephalography/magnetoencephalography) in patient populations with preclinical research has contributed to the conceptualization of schizophrenia as a disorder associated with aberrant neural dynamics and disturbances in excitation/inhibition (E/I) balance parameters. Specifically, I will propose that recent technological and analytic advances in MEG provide novel opportunities to address these fundamental questions as well as establish important links with translational research.

We have carried out several studies which have tested the importance of neural oscillations in the pathophysiology of schizophrenia through a combination of MEG-measurements in ScZ-patients and pharmacological manipulations in healthy volunteers which target the NMDA-receptor. These results highlight a pronounced impairment in high-frequency activity in both chronic and unmedicated patients which could provide novel insights into basic circuit mechanisms underlying cognitive and perceptual dysfunctions.

Our recent work has employed MEG to understand the developmental trajectory of neural oscillations during adolescence and the possibility to develop a biomarker for early detection and diagnosis of ScZ. We found marked changes in the amplitude of high-frequency oscillations and synchrony that were particularly pronounced during the transition from adolescence to adulthood. Moreover, data from participants meeting ultra-high risk criteria for psychosis suggest that signatures of aberrant neuronal dynamics are already present prior to the onset of psychosis, highlighting the importance of advancing biomarkers for early intervention and diagnosis.

Yuli Fradkin
Department of Psychiatry
Robert Wood Johnson Medical School
Rutgers University Behavioral Health Care

Major Depressive Disorder (MDD) is recognized to be heterogeneous in terms of brain structure abnormality findings across studies, which might reflect previously unstudied traits that confer variability to neuroimaging measurements. The purpose of this study was to examine the relationships between trait impulsivity and MDD diagnosis on adolescent brain structure. We predicted that adolescents with depression who were high on trait impulsivity would have more abnormal cortical structure than depressed patients or non-MDD who were low on impulsivity. We recruited 58 subjects, including 29 adolescents (ages 12-19) with a primary DSM-IV diagnosis of MDD and a history of suicide attempt and 29 demographically-matched healthy control participants. Our GLM-based analyses sought to describe differences in the linear relationships between cortical thickness and impulsivity trait levels. As hypothesized, we found significant moderation effects in rostral middle frontal gyrus and paracentral lobule cortical thickness for different subscales of the Barratt Impulsiveness Scale. However, although these brain-behavior relationships differed between diagnostic study groups, they were not simple additive effects as we had predicted. In conclusion, the findings confirm that dimensions of impulsivity have discrete neural correlates, and show that relationships between impulsivity and brain structure are expressed differently in adolescents with MDD compared to non-MDD adolescents.

Eran Eldar
Wellcome Trust Centre for Neuroimaging
Max Planck UCL Centre for Computational Psychiatry and Ageing Research
University College London

Unexpected rewards impact our mood, which may in turn impact our evaluation of subsequent rewards. I will show how this two-way interaction between mood and reward learning may serve an adaptive role, ‘correcting’ learning to account for widespread changes in reward availability in the environment. I will then present theoretical and experimental evidence indicating that this mechanism can also have maladaptive consequences, in particular by engendering mood instability that may contribute to psychiatric mood disorders.

Peter Hitchcock
Department of Psychology
Drexel University

I will contrast two views of mental health problems: the malfunction and bounded rationality views. The malfunction view states that mental health problems are due to faulty machinery which produces irrational behavior. The bounded rationality view states that people with mental health difficulties act in a rational, which is to say goal-consistent, manner, but that their goals are sometimes maladaptive due to the boundedness of rationality (where boundedness is due to the limits of knowledge and computational capacity, and to the opportunity cost inherent in action selection). According to the malfunction view, biology is the science of prime relevance to mental health; according to the bounded rationality view, the science of intelligence is most relevant. Computational researchers have a long history of adopting a stance of bounded rationality when characterizing new problems and describing human behavior. What is less well-known is that cognitive-behavioral therapy researchers adopt a strikingly similar stance during case formulation and treatment development, as I will show through examples. The aim of this talk is to brainstorm with the CCNP community about whether this similar approach to solving problems offers opportunities for generating new predictions and for making progress on problems in common.